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A fast and robust algorithm to count topologically persistent holes in noisy clouds (2014)
Conference Proceeding
Kurlin, V. (2014). A fast and robust algorithm to count topologically persistent holes in noisy clouds.

Preprocessing a 2D image often produces a noisy cloud of interest points. We study the problem of counting holes in noisy clouds in the plane. The holes in a given cloud are quantified by the topological persistence of their boundary contours when th... Read More about A fast and robust algorithm to count topologically persistent holes in noisy clouds.

Computing a configuration skeleton for motion planning of two round robots on a metric graph (2014)
Conference Proceeding
Kurlin, V., & Safi-Samghabadi, M. (2014). Computing a configuration skeleton for motion planning of two round robots on a metric graph. In International Conference on Robotics and Mechatronics Conference (ICROM 2014) : digest book : October 15-17, 2014, Khajeh Nasir Toosi University, Tehran, Iran (723-729). https://doi.org/10.1109/icrom.2014.6990989

A connected metric graph G with n vertices and without loops and multiple edges is given as an n × n-matrix whose entry aij is the length of a single edge between vertices i ≠ j. A robot in the metric graph G is the metric ball with a center x ϵ G an... Read More about Computing a configuration skeleton for motion planning of two round robots on a metric graph.

Numerical inversion of SRNFs for efficient elastic shape analysis of star-shaped objects (2014)
Conference Proceeding
Xie, Q., Jermyn, I., Kurtek, S., & Srivastava, A. (2014). Numerical inversion of SRNFs for efficient elastic shape analysis of star-shaped objects. In D. Fleet, T. Pajdla, B. Schiele, & T. Tuytelaars (Eds.), Computer vision - ECCV 2014 : 13th European Conference Zurich, Switzerland, September 6-12, 2014 ; proceedings, part V (485-499). https://doi.org/10.1007/978-3-319-10602-1_32

The elastic shape analysis of surfaces has proven useful in several application areas, including medical image analysis, vision, and graphics. This approach is based on defining new mathematical representations of parameterized surfaces, including th... Read More about Numerical inversion of SRNFs for efficient elastic shape analysis of star-shaped objects.

Using Storm for scaleable sequential statistical inference. (2014)
Conference Proceeding
Wilson, S., Mai, T., Cogan, P., Bhattacharya, A., Robles-Sánchez, O., Aslett, L., …Roetzer, G. (2014). Using Storm for scaleable sequential statistical inference. In M. Gilli, G. González-Rodríguez, & A. Nieto-Reyes (Eds.), Proceedings of COMPSTAT 2014: 21st International Conference on Computational Statistics (hosting the 5th IASC World Conference): Geneva, Switzerland, August 19–22, 2014 (103-109)

This article describes Storm, an environment for doing streaming data analysis. Two examples of sequential data analysis — computation of a running summary statistic and sequential updating of a posterior distribution — are implemented and their perf... Read More about Using Storm for scaleable sequential statistical inference..

Multinomial logistic regression on Markov chains for crop rotation modelling (2014)
Conference Proceeding
Paton, L., Troffaes, M. C., Boatman, N., Hussein, M., & Hart, A. (2014). Multinomial logistic regression on Markov chains for crop rotation modelling. In Information processing and management of uncertainty in knowledge-based systems : 15th International Conference, IPMU 2014, Montpellier, France, July 15-19, 2014 ; proceedings, part III (476-485). https://doi.org/10.1007/978-3-319-08852-5_49

Often, in dynamical systems such as farmer’s crop choices, the dynamics are driven by external non-stationary factors, such as rainfall, temperature and agricultural input and output prices. Such dynamics can be modelled by a non-stationary Markov ch... Read More about Multinomial logistic regression on Markov chains for crop rotation modelling.

A Note on Learning Dependence Under Severe Uncertainty (2014)
Conference Proceeding
Troffaes, M. C., Coolen, F. P., & Destercke, S. (2014). A Note on Learning Dependence Under Severe Uncertainty. In Information processing and management of uncertainty in knowledge-based systems : 15th International Conference, IPMU 2014, Montpellier, France, July 15-19, 2014 ; proceedings, part III (498-507). https://doi.org/10.1007/978-3-319-08852-5_51

We propose two models, one continuous and one categorical, to learn about dependence between two random variables, given only limited joint observations, but assuming that the marginals are precisely known. The continuous model focuses on the Gaussia... Read More about A Note on Learning Dependence Under Severe Uncertainty.

Bivariate Estimation of Distribution Algorithms for Protein Structure Prediction (2014)
Conference Proceeding
Bonetti, D., Delbem, A., & Einbeck, J. (2014). Bivariate Estimation of Distribution Algorithms for Protein Structure Prediction. In T. Kneib, F. Sobotka, J. Fahrenholz, & H. Irmer (Eds.), 29th International Workshop on Statistical Modelling, 14-18 July 2014, Göttingen, Germany ; proceedings (15-18)

A real-valued bivariate ‘Estimation of Distribution Algorithm’ specific for the ab initio and full-atom Protein Structure Prediction problem is proposed. It is known that this is a multidimensional and multimodal problem. In order to deal with the mu... Read More about Bivariate Estimation of Distribution Algorithms for Protein Structure Prediction.

Bayesian shape modelling of cross-sectional geological data (2014)
Conference Proceeding
Tsiftsi, T., Jermyn, I., & Einbeck, J. (2014). Bayesian shape modelling of cross-sectional geological data. In K. Thomas, S. Fabian, F. Jan, & I. Henriette (Eds.), 29th International Workshop on Statistical Modelling, 14-18 July 2014, Göttingen, Germany ; proceedings (161-164)

Shape information is of great importance in many applications. For example, the oil-bearing capacity of sand bodies, the subterranean remnants of ancient rivers, is related to their cross-sectional shapes. The analysis of these shapes is therefore of... Read More about Bayesian shape modelling of cross-sectional geological data.

A study of online and blockwise updating of the EM algorithm for Gaussian mixtures (2014)
Conference Proceeding
Einbeck, J., & Bonetti, D. (2014). A study of online and blockwise updating of the EM algorithm for Gaussian mixtures. In T. Kneib, F. Sobotka, J. Fahrenholz, & H. Irmer (Eds.), 29th International Workshop on Statistical Modelling, 14-18 July 2014, Göttingen, Germany ; proceedings (35-38)

A variant of the EM algorithm for the estimation of multivariate Gaussian mixtures, which allows for online as well as blockwise updating of sequentially obtained parameter estimates, is investigated. Several dierent update schemes are considered and... Read More about A study of online and blockwise updating of the EM algorithm for Gaussian mixtures.